points_dir = os.path.commonpath(points_paths)
points_relpaths = [os.path.relpath(p, points_dir) for p in points_paths]
else:
points_relpaths = [os.path.basename(p) for p in points_paths]
torch.manual_seed(24)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(24)
view_sampler = SphericalSampler(300, 'SPIRAL')
points = view_sampler.points
save_ply(points,'example_data/pointclouds/spiral_300.ply',normals=points)
scene = readScene(opt.source, device="cpu")
opt.genCamera = 300
if opt.genCamera > 0:
camSampler = CameraSampler(opt.genCamera, opt.camOffset, opt.camFocalLength, points=scene.cloud.localPoints,
camWidth=opt.width, camHeight=opt.height, filename="example_data/pointclouds/spiral_300.ply")
camSampler.closer = False
with torch.no_grad():
splatter = createSplatter(opt, scene=scene)
#splatter.shading = 'depth'
if opt.genCamera > 0:
cameras = []
for i in range(opt.genCamera):
cam = next(camSampler)
cameras.append(cam)
splatter.initCameras(cameras=cameras)
writeScene(scene, os.path.join(expr_dir, 'final_scene.json'),
os.path.join(expr_dir, 'final_cloud.ply'))
if __name__ == "__main__":
opt = DeformationOptions().parse()
# reproducability
torch.manual_seed(24)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(24)
# load scenes
refScene = readScene(opt.ref, device=torch.device("cpu"))
scene = readScene(opt.source, device=torch.device("cpu"))
if opt.baseline:
refScene.cloud.shading = scene.cloud.shading = "depth"
scene.cloud.shading = refScene.cloud.shading
scene.pointlightPositions = refScene.pointlightPositions
scene.pointlightColors = refScene.pointlightColors
scene.sunDirections = refScene.sunDirections
scene.sunColors = refScene.sunColors
scene.ambientLight = refScene.ambientLight
trainShapeOnImage(scene,
refScene,
opt,
baseline=opt.baseline,